MétaCan
Menu
Back to cohort
Record W2587762331 · doi:10.1136/esmoopen-2016-000123

Adjuvant treatment following radical cystectomy for muscle-invasive urothelial carcinoma and variant histologies: Is there a role for radiotherapy?

2017· article· en· W2587762331 on OpenAlexfundno aff
Kevin Lee Min Chua, Grace Kusumawidjaja, Jure Murgić, Melvin L.K. Chua

Bibliographic record

VenueESMO Open · 2017
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
FundersNational Medical Research CouncilMedical Research CouncilCanadian Urological Oncology Group
KeywordsCystectomyMedicineRadiation therapyBladder cancerAdjuvantOncologyOccultInternal medicineCancerPathology

Abstract

fetched live from OpenAlex

Comprehensive molecular characterisation of muscle-invasive urothelial carcinoma and variant histological subtypes has led to the identification of recurrent driver mutations that are distinct in these aggressive subgroups of bladder cancer. While distant metastasis dominates as a pattern of relapse following radical cystectomy or chemoradiotherapy, loco-regional control rates are also suboptimal with single modality local treatment, and likewise, harbour equivocal implications on the long-term prognosis of patients. The role of adjuvant radiotherapy for optimising disease control within the pelvis is controversial, with limited evidence to support its efficacy. Herein, we present a stepwise review on adjuvant radiotherapy post-cystectomy; first, discussing the evidence to date supporting the concept that adjuvant radiotherapy is effective in targeting occult metastases within the pelvis, and adds to the benefits of adjuvant chemotherapy. Next, we outlined the principles underlying the definition of radiotherapy target volumes. To conclude, we addressed the need for appropriate patient stratification for treatment intensification, based on existing clinical models and novel molecular indices of aggression in muscle-invasive urothelial cancers and variant histological subtypes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.339
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueESMO OpenSame topicBladder and Urothelial Cancer TreatmentsFrench-language works237,207